#Convert("all_samples.h5ad", dest = "h5seurat", overwrite = T)
seurat_anndata = LoadH5Seurat("all_samples.h5seurat", assays = "RNA")
## Validating h5Seurat file
## Initializing RNA with data
## Adding counts for RNA
## Adding scale.data for RNA
## Adding feature-level metadata for RNA
## Adding reduction harmony
## Adding cell embeddings for harmony
## Adding miscellaneous information for harmony
## Adding reduction pca
## Adding cell embeddings for pca
## Adding feature loadings for pca
## Adding miscellaneous information for pca
## Adding reduction umap
## Adding cell embeddings for umap
## Adding miscellaneous information for umap
## Adding command information
## Adding cell-level metadata
levels(seurat_anndata@meta.data$sample)
## [1] "8356" "11522" "11817" "11918" "12889" "12929" "12935" "13634" "13636"
## [10] "13774" "14428" "14958" "14965" "15002" "15467"
clinical.data <- data.frame(read.csv("/home/marcelo/LungPredict1/RawFiles/ColumnData_Vanderbilt.csv", row.names = 1))
clinical.data <- clinical.data[,c(1,5,6,9,11,13)]
coldata = clinical.data[clinical.data$pt_ID%in%levels(seurat_anndata@meta.data$sample),]
#14958 14965 11817 13634 15467 12929 8356 12889 15002
#"R3388_YZ_1" "R3388_YZ_2" "R3388_YZ_10" "R3388_YZ_11" "R3388_YZ_18" "R3388_YZ_28" "R3388_YZ_56" "R4163_YZ_16" "R4163_YZ_25"
Clustering
DimPlot(seurat_anndata, reduction="umap")
ElbowPlot(seurat_anndata) #determine dimensionality of data
seurat_anndata = FindNeighbors(seurat_anndata, dims = 1:20)
## Computing nearest neighbor graph
## Computing SNN
seurat_anndata = FindClusters(seurat_anndata, resolution = 1)
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 44876
## Number of edges: 1487565
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8737
## Number of communities: 25
## Elapsed time: 10 seconds
DimPlot(seurat_anndata, reduction="umap", label = T)
Manual annotation
Find markers (one cluster vs all others)
markers = FindAllMarkers(seurat_anndata,
logfc.threshold = 0.25, #min logfc
min.pct = 0.1, #genes that are detected on 50% frequency across clusters
only.pos = T,
test.use = 'wilcox',
slot = 'counts')
## Calculating cluster 0
## Calculating cluster 1
## Calculating cluster 2
## Calculating cluster 3
## Calculating cluster 4
## Calculating cluster 5
## Calculating cluster 6
## Calculating cluster 7
## Calculating cluster 8
## Calculating cluster 9
## Calculating cluster 10
## Calculating cluster 11
## Calculating cluster 12
## Calculating cluster 13
## Calculating cluster 14
## Calculating cluster 15
## Calculating cluster 16
## Calculating cluster 17
## Calculating cluster 18
## Calculating cluster 19
## Calculating cluster 20
## Calculating cluster 21
## Calculating cluster 22
## Calculating cluster 23
## Calculating cluster 24
head(markers)
## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## MS4A1 0 1.559624 0.904 0.038 0 0 MS4A1
## HLA-DRA 0 1.519554 0.987 0.267 0 0 HLA-DRA
## HLA-DQA1 0 1.241250 0.946 0.277 0 0 HLA-DQA1
## CD79A 0 1.217081 0.841 0.136 0 0 CD79A
## CD83 0 1.139373 0.771 0.156 0 0 CD83
## HLA-DQB1 0 1.119662 0.920 0.309 0 0 HLA-DQB1
features = c('NKG7', 'IL7R', 'HLA-DRA','IGHG1','BATF','IGFBP7', 'TYROBP', 'RAMP2','TYMS','TPSAB1','WFDC2','SFRP21')
RidgePlot(seurat_anndata, features = features, ncol = 3)
## Warning in FetchData.Seurat(object = object, vars = features, slot = slot): The
## following requested variables were not found: SFRP21
## Picking joint bandwidth of 0.0686
## Picking joint bandwidth of 0.114
## Picking joint bandwidth of 0.0942
## Picking joint bandwidth of 0.0628
## Picking joint bandwidth of 0.064
## Picking joint bandwidth of 0.055
## Picking joint bandwidth of 0.0322
## Picking joint bandwidth of 0.00837
## Picking joint bandwidth of 0.00972
## Picking joint bandwidth of 0.011
## Picking joint bandwidth of 0.0369
VlnPlot(seurat_anndata, features = features)
## Warning in FetchData.Seurat(object = object, vars = features, slot = slot): The
## following requested variables were not found: SFRP21
FeaturePlot(seurat_anndata, features = features)
## Warning in FetchData.Seurat(object = object, vars = c(dims, "ident", features),
## : The following requested variables were not found: SFRP21
Automatic cell annotation (ATLAS)
ref = celldex::HumanPrimaryCellAtlasData()
## snapshotDate(): 2022-04-26
## see ?celldex and browseVignettes('celldex') for documentation
## loading from cache
## see ?celldex and browseVignettes('celldex') for documentation
## loading from cache
single_counts = GetAssayData(seurat_anndata, slot='counts')
pred = SingleR(test = single_counts,
ref = ref,
labels = ref$label.main)
## Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
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## Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
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head(pred)
## DataFrame with 6 rows and 5 columns
## scores first.labels
## <matrix> <character>
## AAACCTGAGCTTTGGT-1 0.128861:0.245469:0.212046:... NK_cell
## AAACCTGCACCAGGCT-1 0.139778:0.303035:0.281091:... T_cells
## AAACGGGGTACTCTCC-1 0.147528:0.257401:0.271606:... DC
## AAACGGGTCGACCAGC-1 0.111915:0.261614:0.254842:... T_cells
## AAAGCAAAGACAAGCC-1 0.159956:0.297482:0.273721:... NK_cell
## AAAGCAACAAGCCCAC-1 0.132399:0.278978:0.237570:... T_cells
## tuning.scores labels pruned.labels
## <DataFrame> <character> <character>
## AAACCTGAGCTTTGGT-1 0.286362:0.188706 NK_cell NK_cell
## AAACCTGCACCAGGCT-1 0.368637:0.247717 T_cells T_cells
## AAACGGGGTACTCTCC-1 0.339815:0.295807 DC DC
## AAACGGGTCGACCAGC-1 0.285696:0.201506 T_cells T_cells
## AAAGCAAAGACAAGCC-1 0.231436:0.109418 T_cells T_cells
## AAAGCAACAAGCCCAC-1 0.327587:0.210946 T_cells T_cells
seurat_anndata$singleR.labels = pred$labels[match(rownames(seurat_anndata@meta.data), rownames(pred))]
DimPlot(seurat_anndata, reduction = 'umap', group.by = 'singleR.labels')
head(pred$scores)
## Astrocyte B_cell BM BM & Prog. Chondrocytes CMP DC
## [1,] 0.1288609 0.2454694 0.2120456 0.1785377 0.1596201 0.2348847 0.2354978
## [2,] 0.1397776 0.3030350 0.2810913 0.2213133 0.1728824 0.2685374 0.2804226
## [3,] 0.1475280 0.2574010 0.2716059 0.1651107 0.1920963 0.2424944 0.4121792
## [4,] 0.1119149 0.2616144 0.2548422 0.1872333 0.1379515 0.2385703 0.2368478
## [5,] 0.1599564 0.2974818 0.2737211 0.1974025 0.1841983 0.2644691 0.2769523
## [6,] 0.1323993 0.2789778 0.2375703 0.1940324 0.1651088 0.2457796 0.2610297
## Embryonic_stem_cells Endothelial_cells Epithelial_cells Erythroblast
## [1,] 0.11055616 0.1720191 0.1649112 0.1711534
## [2,] 0.14682796 0.2061413 0.1984771 0.2102717
## [3,] 0.11516413 0.2243761 0.2078711 0.1646774
## [4,] 0.09878323 0.1655049 0.1424403 0.1646180
## [5,] 0.15609302 0.1984647 0.1890662 0.1865274
## [6,] 0.12805750 0.1919904 0.1725622 0.1855369
## Fibroblasts Gametocytes GMP Hepatocytes HSC_-G-CSF HSC_CD34+
## [1,] 0.1482040 0.1447390 0.2480521 0.1421683 0.2486675 0.2373476
## [2,] 0.1682255 0.1479854 0.2823766 0.1834610 0.3267963 0.2644097
## [3,] 0.2031987 0.1604163 0.2833434 0.2027688 0.3257528 0.2971054
## [4,] 0.1292689 0.1043757 0.2480834 0.1207127 0.2895977 0.2267591
## [5,] 0.1721161 0.1500515 0.2796854 0.1733605 0.3159671 0.2598502
## [6,] 0.1643852 0.1329930 0.2548679 0.1562751 0.2895931 0.2345147
## iPS_cells Keratinocytes Macrophage MEP Monocyte MSC Myelocyte
## [1,] 0.1394411 0.1544705 0.2300599 0.2011366 0.2573516 0.1407319 0.2235121
## [2,] 0.1524905 0.1699264 0.2840576 0.2351096 0.3025898 0.1558683 0.2639280
## [3,] 0.1636068 0.1870450 0.4078145 0.1920928 0.3987618 0.1676612 0.2898561
## [4,] 0.1208349 0.1025808 0.2383048 0.2018317 0.2600235 0.1223021 0.2282195
## [5,] 0.1606894 0.1669182 0.2726076 0.2209953 0.2963006 0.1623042 0.2597675
## [6,] 0.1474047 0.1368754 0.2519917 0.2120243 0.2768807 0.1484591 0.2398599
## Neuroepithelial_cell Neurons Neutrophils NK_cell Osteoblasts Platelets
## [1,] 0.09700509 0.1359534 0.2537397 0.3069176 0.1431023 0.1829822
## [2,] 0.11660883 0.1569972 0.2927893 0.3508412 0.1509951 0.2332163
## [3,] 0.09467130 0.1909475 0.3675563 0.2717566 0.1831689 0.2175084
## [4,] 0.07989130 0.1365315 0.2374779 0.3070698 0.1262604 0.2248319
## [5,] 0.12961705 0.1834586 0.2782209 0.3644448 0.1620286 0.2414834
## [6,] 0.10334531 0.1631646 0.2559404 0.3233688 0.1526883 0.2199729
## Pre-B_cell_CD34- Pro-B_cell_CD34+ Pro-Myelocyte Smooth_muscle_cells
## [1,] 0.2703212 0.2400679 0.2212136 0.1496663
## [2,] 0.3417694 0.2794768 0.2570039 0.1643441
## [3,] 0.3381400 0.2465303 0.2703544 0.1918451
## [4,] 0.2981949 0.2452074 0.2285450 0.1330463
## [5,] 0.3316083 0.2753085 0.2575988 0.1716137
## [6,] 0.3012617 0.2530245 0.2269616 0.1630644
## T_cells Tissue_stem_cells
## [1,] 0.2784824 0.1696429
## [2,] 0.3633034 0.1775285
## [3,] 0.2523547 0.2081034
## [4,] 0.3129331 0.1419393
## [5,] 0.3636598 0.1965812
## [6,] 0.3329824 0.1699385
plotScoreHeatmap(pred)
## Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
## or useNames = TRUE.
## Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
## or useNames = TRUE.
## Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
## or useNames = TRUE.
plotDeltaDistribution(pred)
## Warning: useNames = NA is deprecated. Instead, specify either useNames = TRUE
## or useNames = TRUE.
## Warning: Groups with fewer than two data points have been dropped.
## Warning in max(data$density): no non-missing arguments to max; returning -Inf
## Warning: Computation failed in `stat_ydensity()`
## Caused by error in `$<-.data.frame`:
## ! replacement has 1 row, data has 0
## Warning: Groups with fewer than two data points have been dropped.
## Warning in max(data$density): no non-missing arguments to max; returning -Inf
## Warning: Computation failed in `stat_ydensity()`
## Caused by error in `$<-.data.frame`:
## ! replacement has 1 row, data has 0
tab = table(Assigned = pred$labels, Clusters = seurat_anndata$seurat_clusters)
pheatmap(log10(tab+10), color=colorRampPalette(c('white', 'blue'))(10))
Extract NK cluster (cluster = 8) for further analysis
Idents(seurat_anndata) <- "seurat_clusters"
NK = subset(seurat_anndata, idents = "8", invert = FALSE)
DimPlot(NK, reduction="umap")
NK clustering
ElbowPlot(NK) #determine dimensionality of data
NK<- FindNeighbors(NK, dims = 1:10)
## Computing nearest neighbor graph
## Computing SNN
NK<- FindClusters(NK, algorithm= 1, resolution = 0.15, verbose = T, graph.name = "RNA_snn")
## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 2000
## Number of edges: 71175
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.8760
## Number of communities: 2
## Elapsed time: 0 seconds
NK<- RunUMAP(NK, dims = 1:10)
## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session
## 18:37:50 UMAP embedding parameters a = 0.9922 b = 1.112
## 18:37:50 Read 2000 rows and found 10 numeric columns
## 18:37:50 Using Annoy for neighbor search, n_neighbors = 30
## 18:37:50 Building Annoy index with metric = cosine, n_trees = 50
## 0% 10 20 30 40 50 60 70 80 90 100%
## [----|----|----|----|----|----|----|----|----|----|
## **************************************************|
## 18:37:51 Writing NN index file to temp file /tmp/Rtmp0ydrFV/file203b83f64acf9
## 18:37:51 Searching Annoy index using 1 thread, search_k = 3000
## 18:37:51 Annoy recall = 100%
## 18:37:52 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 18:37:52 Initializing from normalized Laplacian + noise (using irlba)
## 18:37:52 Commencing optimization for 500 epochs, with 86322 positive edges
## 18:37:54 Optimization finished
DimPlot(NK, reduction="umap", label = T)
Differential expressed markers across clusters
markers = FindMarkers(NK, ident.1 = "0", ident.2 = "1")
head(markers)
## p_val avg_log2FC pct.1 pct.2 p_val_adj
## GZMB 2.989360e-61 0.5385857 0.848 0.528 7.146064e-57
## FCGR3A 1.910441e-42 0.5595423 0.501 0.206 4.566909e-38
## PRF1 6.566120e-34 0.3865737 0.795 0.549 1.569631e-29
## IL7R 2.019473e-32 -0.5227357 0.222 0.456 4.827550e-28
## GNLY 1.811075e-28 0.3279821 0.882 0.685 4.329376e-24
## CTSW 2.055846e-28 0.2773990 0.914 0.732 4.914500e-24
features = c('GZMB', 'FCGR3A', 'PRF1','IL7R','GNLY','CTSW')
#features = c('CCL3', 'TIGIT', 'CD69','IRF8','IL2RB','CD160') markers from Pierre-Paul
FeaturePlot(NK, features = features)
VlnPlot(NK, features = features)
RidgePlot(NK, features = features, ncol = 2)
## Picking joint bandwidth of 0.264
## Picking joint bandwidth of 0.127
## Picking joint bandwidth of 0.241
## Picking joint bandwidth of 0.156
## Picking joint bandwidth of 0.345
## Picking joint bandwidth of 0.216
saveRDS(NK, "nk.rds")